I want to express my interest in the Solutions Engineer position. I enjoy using R to find answers to biological questions, and I find joy in helping others use R to solve global problems.
I remember seeing ggplot for the first time during a 3 hour Introduction to R workshop at The
University of Texas at Austin in 2011. R was only being taught in a few classes, but a
burgeoning community of data scientists and bioinformaticians were filling gaps in the
curriculum by offering hands-on workshops. Through these workshops and my involvement with
The Carpentries, I learned how to use and teach tools such as high-performance computing
systems, R, and Git to conduct open and reproducible research.
After obtaining my PhD, I went to Argentina and gave a talk at RLadies Buenos Aires called
“Usando y Enseñando R para Investigación Reproducible / Using and Teaching R for
Reproducible Research”. Later, two of the attendees helped me host an in-person hackathon to
translate Data Carpentry R lessons into Spanish, which was something I had only been doing
remotely via Slack and GitHub. Many members of this LatinR community also helped
translate R for Data Science (R4DS) into Spanish, and I co-authored the datos package that
provides translations of datasets used in R4DS to make these resources more accessible to
diverse communities of R users that don’t speak English as a first language.
My postdoctoral work in Dr. Titus Brown’s lab has given me the most insight into the latest in cloud computing and data science technologies. In 2018, I helped coordinate the communication and community management for a national project that brought together 500 scientist and engineers from a dozen institutions to build a cloud-based “data commons” with the computational capacity to make biomedical data and metadata findable, accessible, interoperable, and reusable (FAIR) to approved users. Funding for this pilot project was cut short, but the technical skills and personal connections I made were long-lasting.
In Dr. Rebecca Calisi-Rodíguez’s lab, I’ve been experimenting with novel ways to communicate
science with R. The “transcriptional symphony” is a metaphor used to describe coordinated
changes in gene expression, but these changes are almost always presented with graphs and
stats. I’ve created a Shiny app called “Musical Genes” that converts data into sound using the R
package sonify so that users can listen to the sounds of gene expression changing over time. I
hope this tool makes the data more accessible to those with vision impairments and inspires
others to think about novel ways to make their data accessible to diverse audiences.
I look forward to learning more about the Solutions Engineer position and how I can contribute to the growth of the products and communities supported by RStudio.